Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations451
Missing cells843
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.0 KiB
Average record size in memory120.3 B

Variable types

Text6
Categorical3
Numeric6

Alerts

Camere is highly overall correlated with Piani totali and 1 other fieldsHigh correlation
Codice via is highly overall correlated with ZDHigh correlation
Piani totali is highly overall correlated with Camere and 1 other fieldsHigh correlation
Posti letto is highly overall correlated with Camere and 1 other fieldsHigh correlation
ZD is highly overall correlated with Codice viaHigh correlation
Tipo via is highly imbalanced (57.2%) Imbalance
Tipo attività struture extra is highly imbalanced (62.8%) Imbalance
Tipo via has 14 (3.1%) missing values Missing
Descrizione via has 14 (3.1%) missing values Missing
Civico has 30 (6.7%) missing values Missing
Codice via has 14 (3.1%) missing values Missing
ZD has 14 (3.1%) missing values Missing
Camere piano has 106 (23.5%) missing values Missing
Categoria has 7 (1.6%) missing values Missing
Insegna has 10 (2.2%) missing values Missing
Piani totali has 264 (58.5%) missing values Missing
Piano piano has 252 (55.9%) missing values Missing
Posti letto per piano has 106 (23.5%) missing values Missing
Tipo attività struture extra has 10 (2.2%) missing values Missing
Civico is highly skewed (γ1 = 20.50862033) Skewed

Reproduction

Analysis started2024-12-14 17:12:09.096392
Analysis finished2024-12-14 17:12:11.948071
Duration2.85 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Ubicazione
Text

Distinct438
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:12.238593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length123
Median length60
Mean length33.545455
Min length22

Characters and Unicode

Total characters15129
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique426 ?
Unique (%)94.5%

Sample

1st rowALZ NAVIGLIO GRANDE N. 8 (z.d. 6)
2nd rowcodvia 0000 num.024 ; ()
3rd rowCSO BUENOS AIRES N. 18 (z.d. 3)
4th rowCSO BUENOS AIRES N. 26 (z.d. 3)
5th rowCSO BUENOS AIRES N. 2 (z.d. 3)
ValueCountFrequency (%)
z.d 443
 
14.0%
n 425
 
13.5%
via 343
 
10.9%
3 127
 
4.0%
2 98
 
3.1%
1 87
 
2.8%
8 59
 
1.9%
vle 48
 
1.5%
4 48
 
1.5%
9 47
 
1.5%
Other values (614) 1431
45.3%
2024-12-14T18:12:12.584040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2707
17.9%
. 1355
 
9.0%
A 1046
 
6.9%
I 1001
 
6.6%
N 823
 
5.4%
O 700
 
4.6%
V 482
 
3.2%
E 481
 
3.2%
d 456
 
3.0%
z 452
 
3.0%
Other values (56) 5626
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2707
17.9%
. 1355
 
9.0%
A 1046
 
6.9%
I 1001
 
6.6%
N 823
 
5.4%
O 700
 
4.6%
V 482
 
3.2%
E 481
 
3.2%
d 456
 
3.0%
z 452
 
3.0%
Other values (56) 5626
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2707
17.9%
. 1355
 
9.0%
A 1046
 
6.9%
I 1001
 
6.6%
N 823
 
5.4%
O 700
 
4.6%
V 482
 
3.2%
E 481
 
3.2%
d 456
 
3.0%
z 452
 
3.0%
Other values (56) 5626
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2707
17.9%
. 1355
 
9.0%
A 1046
 
6.9%
I 1001
 
6.6%
N 823
 
5.4%
O 700
 
4.6%
V 482
 
3.2%
E 481
 
3.2%
d 456
 
3.0%
z 452
 
3.0%
Other values (56) 5626
37.2%

Tipo via
Categorical

Imbalance  Missing 

Distinct8
Distinct (%)1.8%
Missing14
Missing (%)3.1%
Memory size3.7 KiB
VIA
331 
VLE
47 
CSO
 
25
PZA
 
24
PLE
 
4
Other values (3)
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1311
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowALZ
2nd rowCSO
3rd rowCSO
4th rowCSO
5th rowCSO

Common Values

ValueCountFrequency (%)
VIA 331
73.4%
VLE 47
 
10.4%
CSO 25
 
5.5%
PZA 24
 
5.3%
PLE 4
 
0.9%
LGO 3
 
0.7%
GLL 2
 
0.4%
ALZ 1
 
0.2%
(Missing) 14
 
3.1%

Length

2024-12-14T18:12:12.655571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T18:12:12.822601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
via 331
75.7%
vle 47
 
10.8%
cso 25
 
5.7%
pza 24
 
5.5%
ple 4
 
0.9%
lgo 3
 
0.7%
gll 2
 
0.5%
alz 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
V 378
28.8%
A 356
27.2%
I 331
25.2%
L 59
 
4.5%
E 51
 
3.9%
P 28
 
2.1%
O 28
 
2.1%
S 25
 
1.9%
C 25
 
1.9%
Z 25
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 378
28.8%
A 356
27.2%
I 331
25.2%
L 59
 
4.5%
E 51
 
3.9%
P 28
 
2.1%
O 28
 
2.1%
S 25
 
1.9%
C 25
 
1.9%
Z 25
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 378
28.8%
A 356
27.2%
I 331
25.2%
L 59
 
4.5%
E 51
 
3.9%
P 28
 
2.1%
O 28
 
2.1%
S 25
 
1.9%
C 25
 
1.9%
Z 25
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 378
28.8%
A 356
27.2%
I 331
25.2%
L 59
 
4.5%
E 51
 
3.9%
P 28
 
2.1%
O 28
 
2.1%
S 25
 
1.9%
C 25
 
1.9%
Z 25
 
1.9%

Descrizione via
Text

Missing 

Distinct300
Distinct (%)68.6%
Missing14
Missing (%)3.1%
Memory size3.7 KiB
2024-12-14T18:12:13.129403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length21
Mean length13.146453
Min length4

Characters and Unicode

Total characters5745
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)51.3%

Sample

1st rowNAVIGLIO GRANDE
2nd rowBUENOS AIRES
3rd rowBUENOS AIRES
4th rowBUENOS AIRES
5th rowBUENOS AIRES
ValueCountFrequency (%)
giovanni 19
 
2.4%
antonio 14
 
1.8%
giuseppe 13
 
1.7%
carlo 12
 
1.5%
torriani 10
 
1.3%
nicola 10
 
1.3%
napo 10
 
1.3%
della 9
 
1.1%
dei 8
 
1.0%
battista 8
 
1.0%
Other values (421) 673
85.6%
2024-12-14T18:12:13.423115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 660
11.5%
O 655
11.4%
I 635
11.1%
E 412
 
7.2%
N 384
 
6.7%
R 383
 
6.7%
L 358
 
6.2%
349
 
6.1%
T 274
 
4.8%
C 244
 
4.2%
Other values (17) 1391
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 660
11.5%
O 655
11.4%
I 635
11.1%
E 412
 
7.2%
N 384
 
6.7%
R 383
 
6.7%
L 358
 
6.2%
349
 
6.1%
T 274
 
4.8%
C 244
 
4.2%
Other values (17) 1391
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 660
11.5%
O 655
11.4%
I 635
11.1%
E 412
 
7.2%
N 384
 
6.7%
R 383
 
6.7%
L 358
 
6.2%
349
 
6.1%
T 274
 
4.8%
C 244
 
4.2%
Other values (17) 1391
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 660
11.5%
O 655
11.4%
I 635
11.1%
E 412
 
7.2%
N 384
 
6.7%
R 383
 
6.7%
L 358
 
6.2%
349
 
6.1%
T 274
 
4.8%
C 244
 
4.2%
Other values (17) 1391
24.2%

Civico
Real number (ℝ)

Missing  Skewed 

Distinct90
Distinct (%)21.4%
Missing30
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean126.06176
Minimum1
Maximum41609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:13.506484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median16
Q333
95-th percentile87
Maximum41609
Range41608
Interquartile range (IQR)26

Descriptive statistics

Standard deviation2026.8859
Coefficient of variation (CV)16.078515
Kurtosis420.73437
Mean126.06176
Median Absolute Deviation (MAD)11
Skewness20.50862
Sum53072
Variance4108266.5
MonotonicityNot monotonic
2024-12-14T18:12:13.579485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 22
 
4.9%
6 22
 
4.9%
4 19
 
4.2%
10 16
 
3.5%
3 15
 
3.3%
1 14
 
3.1%
14 14
 
3.1%
12 14
 
3.1%
18 13
 
2.9%
8 13
 
2.9%
Other values (80) 259
57.4%
(Missing) 30
 
6.7%
ValueCountFrequency (%)
1 14
3.1%
2 22
4.9%
3 15
3.3%
4 19
4.2%
5 12
2.7%
6 22
4.9%
7 12
2.7%
8 13
2.9%
9 12
2.7%
10 16
3.5%
ValueCountFrequency (%)
41609 1
0.2%
371 1
0.2%
300 1
0.2%
170 1
0.2%
153 1
0.2%
143 1
0.2%
139 1
0.2%
134 1
0.2%
132 2
0.4%
125 1
0.2%

Codice via
Real number (ℝ)

High correlation  Missing 

Distinct302
Distinct (%)69.1%
Missing14
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean3105.9359
Minimum1
Maximum7505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:13.649644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile336.4
Q11510
median2250
Q35097
95-th percentile7188
Maximum7505
Range7504
Interquartile range (IQR)3587

Descriptive statistics

Standard deviation2140.7002
Coefficient of variation (CV)0.68922871
Kurtosis-0.75254595
Mean3105.9359
Median Absolute Deviation (MAD)1079
Skewness0.68558206
Sum1357294
Variance4582597.3
MonotonicityNot monotonic
2024-12-14T18:12:13.723166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2126 10
 
2.2%
2229 8
 
1.8%
2134 7
 
1.6%
2129 5
 
1.1%
3115 5
 
1.1%
1055 4
 
0.9%
2251 4
 
0.9%
2136 4
 
0.9%
2253 4
 
0.9%
3183 4
 
0.9%
Other values (292) 382
84.7%
(Missing) 14
 
3.1%
ValueCountFrequency (%)
1 1
0.2%
105 1
0.2%
115 1
0.2%
123 1
0.2%
139 1
0.2%
144 1
0.2%
146 1
0.2%
190 1
0.2%
207 2
0.4%
218 1
0.2%
ValueCountFrequency (%)
7505 1
0.2%
7500 1
0.2%
7425 1
0.2%
7420 1
0.2%
7396 1
0.2%
7390 1
0.2%
7382 1
0.2%
7360 1
0.2%
7276 1
0.2%
7272 1
0.2%

ZD
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)2.1%
Missing14
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean4.0755149
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:13.783498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6075525
Coefficient of variation (CV)0.63980936
Kurtosis-0.97167935
Mean4.0755149
Median Absolute Deviation (MAD)2
Skewness0.62967992
Sum1781
Variance6.7993303
MonotonicityNot monotonic
2024-12-14T18:12:13.839635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 111
24.6%
2 75
16.6%
1 69
15.3%
8 44
 
9.8%
9 34
 
7.5%
7 32
 
7.1%
4 30
 
6.7%
5 25
 
5.5%
6 17
 
3.8%
(Missing) 14
 
3.1%
ValueCountFrequency (%)
1 69
15.3%
2 75
16.6%
3 111
24.6%
4 30
 
6.7%
5 25
 
5.5%
6 17
 
3.8%
7 32
 
7.1%
8 44
 
9.8%
9 34
 
7.5%
ValueCountFrequency (%)
9 34
 
7.5%
8 44
 
9.8%
7 32
 
7.1%
6 17
 
3.8%
5 25
 
5.5%
4 30
 
6.7%
3 111
24.6%
2 75
16.6%
1 69
15.3%

Camere
Real number (ℝ)

High correlation 

Distinct148
Distinct (%)32.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean60.002222
Minimum7
Maximum439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:13.913859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9.45
Q116
median32
Q372
95-th percentile231.3
Maximum439
Range432
Interquartile range (IQR)56

Descriptive statistics

Standard deviation70.441133
Coefficient of variation (CV)1.1739754
Kurtosis7.2530647
Mean60.002222
Median Absolute Deviation (MAD)21
Skewness2.5064947
Sum27001
Variance4961.9532
MonotonicityNot monotonic
2024-12-14T18:12:13.993696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 24
 
5.3%
11 15
 
3.3%
14 15
 
3.3%
15 14
 
3.1%
16 11
 
2.4%
13 11
 
2.4%
18 11
 
2.4%
17 10
 
2.2%
12 10
 
2.2%
7 10
 
2.2%
Other values (138) 319
70.7%
ValueCountFrequency (%)
7 10
2.2%
8 6
 
1.3%
9 7
 
1.6%
10 24
5.3%
11 15
3.3%
12 10
2.2%
13 11
2.4%
14 15
3.3%
15 14
3.1%
16 11
2.4%
ValueCountFrequency (%)
439 1
0.2%
423 1
0.2%
420 1
0.2%
328 1
0.2%
327 1
0.2%
323 1
0.2%
320 1
0.2%
313 1
0.2%
305 1
0.2%
302 1
0.2%

Camere piano
Text

Missing 

Distinct207
Distinct (%)60.0%
Missing106
Missing (%)23.5%
Memory size3.7 KiB
2024-12-14T18:12:14.140894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length25
Mean length5.7565217
Min length1

Characters and Unicode

Total characters1986
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)54.2%

Sample

1st row259
2nd row16
3rd row15;11;8
4th row0
5th row4;23;24;24
ValueCountFrequency (%)
0 108
31.3%
8 4
 
1.2%
7 4
 
1.2%
11 4
 
1.2%
13 4
 
1.2%
9 3
 
0.9%
16 3
 
0.9%
3;4 3
 
0.9%
10 3
 
0.9%
12 2
 
0.6%
Other values (197) 207
60.0%
2024-12-14T18:12:14.374000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
; 625
31.5%
1 321
16.2%
0 195
 
9.8%
2 155
 
7.8%
4 134
 
6.7%
6 116
 
5.8%
3 104
 
5.2%
5 88
 
4.4%
7 85
 
4.3%
9 84
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
; 625
31.5%
1 321
16.2%
0 195
 
9.8%
2 155
 
7.8%
4 134
 
6.7%
6 116
 
5.8%
3 104
 
5.2%
5 88
 
4.4%
7 85
 
4.3%
9 84
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
; 625
31.5%
1 321
16.2%
0 195
 
9.8%
2 155
 
7.8%
4 134
 
6.7%
6 116
 
5.8%
3 104
 
5.2%
5 88
 
4.4%
7 85
 
4.3%
9 84
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
; 625
31.5%
1 321
16.2%
0 195
 
9.8%
2 155
 
7.8%
4 134
 
6.7%
6 116
 
5.8%
3 104
 
5.2%
5 88
 
4.4%
7 85
 
4.3%
9 84
 
4.2%

Categoria
Categorical

Missing 

Distinct8
Distinct (%)1.8%
Missing7
Missing (%)1.6%
Memory size3.7 KiB
4
147 
3
135 
1
83 
2
64 
5
 
7
Other values (3)
 
8

Length

Max length14
Median length1
Mean length1.0585586
Min length1

Characters and Unicode

Total characters470
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 147
32.6%
3 135
29.9%
1 83
18.4%
2 64
14.2%
5 7
 
1.6%
l 3
 
0.7%
I 3
 
0.7%
5 STELLE LUSSO 2
 
0.4%
(Missing) 7
 
1.6%

Length

2024-12-14T18:12:14.447042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T18:12:14.525209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4 147
32.8%
3 135
30.1%
1 83
18.5%
2 64
14.3%
5 9
 
2.0%
l 3
 
0.7%
i 3
 
0.7%
stelle 2
 
0.4%
lusso 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
4 147
31.3%
3 135
28.7%
1 83
17.7%
2 64
13.6%
5 9
 
1.9%
S 6
 
1.3%
L 6
 
1.3%
E 4
 
0.9%
4
 
0.9%
l 3
 
0.6%
Other values (4) 9
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 147
31.3%
3 135
28.7%
1 83
17.7%
2 64
13.6%
5 9
 
1.9%
S 6
 
1.3%
L 6
 
1.3%
E 4
 
0.9%
4
 
0.9%
l 3
 
0.6%
Other values (4) 9
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 147
31.3%
3 135
28.7%
1 83
17.7%
2 64
13.6%
5 9
 
1.9%
S 6
 
1.3%
L 6
 
1.3%
E 4
 
0.9%
4
 
0.9%
l 3
 
0.6%
Other values (4) 9
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 147
31.3%
3 135
28.7%
1 83
17.7%
2 64
13.6%
5 9
 
1.9%
S 6
 
1.3%
L 6
 
1.3%
E 4
 
0.9%
4
 
0.9%
l 3
 
0.6%
Other values (4) 9
 
1.9%

Insegna
Text

Missing 

Distinct437
Distinct (%)99.1%
Missing10
Missing (%)2.2%
Memory size3.7 KiB
2024-12-14T18:12:14.766249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length142
Median length33
Mean length15.222222
Min length3

Characters and Unicode

Total characters6713
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)98.2%

Sample

1st rowHOTEL MAISON BORELLA
2nd rowradisson blu hotel milan
3rd rowhotel aurora
4th rowhotel buenos aires
5th rowalbergo fenice
ValueCountFrequency (%)
hotel 279
26.1%
residence 32
 
3.0%
milano 26
 
2.4%
albergo 17
 
1.6%
milan 9
 
0.8%
di 9
 
0.8%
la 8
 
0.7%
san 7
 
0.7%
pensione 7
 
0.7%
starhotels 6
 
0.6%
Other values (533) 671
62.7%
2024-12-14T18:12:15.097705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 746
11.1%
o 657
 
9.8%
640
 
9.5%
a 548
 
8.2%
l 546
 
8.1%
t 476
 
7.1%
i 409
 
6.1%
n 361
 
5.4%
r 344
 
5.1%
h 309
 
4.6%
Other values (57) 1677
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 746
11.1%
o 657
 
9.8%
640
 
9.5%
a 548
 
8.2%
l 546
 
8.1%
t 476
 
7.1%
i 409
 
6.1%
n 361
 
5.4%
r 344
 
5.1%
h 309
 
4.6%
Other values (57) 1677
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 746
11.1%
o 657
 
9.8%
640
 
9.5%
a 548
 
8.2%
l 546
 
8.1%
t 476
 
7.1%
i 409
 
6.1%
n 361
 
5.4%
r 344
 
5.1%
h 309
 
4.6%
Other values (57) 1677
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 746
11.1%
o 657
 
9.8%
640
 
9.5%
a 548
 
8.2%
l 546
 
8.1%
t 476
 
7.1%
i 409
 
6.1%
n 361
 
5.4%
r 344
 
5.1%
h 309
 
4.6%
Other values (57) 1677
25.0%

Piani totali
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)5.9%
Missing264
Missing (%)58.5%
Infinite0
Infinite (%)0.0%
Mean4.6149733
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:15.163120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum17
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4824958
Coefficient of variation (CV)0.53792203
Kurtosis5.1104175
Mean4.6149733
Median Absolute Deviation (MAD)1
Skewness1.409748
Sum863
Variance6.1627853
MonotonicityNot monotonic
2024-12-14T18:12:15.219701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 37
 
8.2%
4 36
 
8.0%
5 24
 
5.3%
7 20
 
4.4%
6 20
 
4.4%
1 17
 
3.8%
2 13
 
2.9%
8 12
 
2.7%
9 5
 
1.1%
17 2
 
0.4%
(Missing) 264
58.5%
ValueCountFrequency (%)
1 17
3.8%
2 13
 
2.9%
3 37
8.2%
4 36
8.0%
5 24
5.3%
6 20
4.4%
7 20
4.4%
8 12
 
2.7%
9 5
 
1.1%
10 1
 
0.2%
ValueCountFrequency (%)
17 2
 
0.4%
10 1
 
0.2%
9 5
 
1.1%
8 12
 
2.7%
7 20
4.4%
6 20
4.4%
5 24
5.3%
4 36
8.0%
3 37
8.2%
2 13
 
2.9%

Piano piano
Text

Missing 

Distinct58
Distinct (%)29.1%
Missing252
Missing (%)55.9%
Memory size3.7 KiB
2024-12-14T18:12:15.371162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length16
Mean length8.4221106
Min length1

Characters and Unicode

Total characters1676
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)18.1%

Sample

1st row1
2nd row1;2;3;4
3rd row2;3;4;5
4th row1;2;3;4;5;6;7
5th row1;2;3;4;5;6;7
ValueCountFrequency (%)
1;2;3;4;5 17
 
8.5%
t;1;2 16
 
8.0%
1;2;3;4;5;6 16
 
8.0%
1;2;3;4;5;6;7 14
 
7.0%
1;2;3;4 14
 
7.0%
1;2;3 11
 
5.5%
1 9
 
4.5%
t;1;2;3 9
 
4.5%
r;1;2 8
 
4.0%
1;2 8
 
4.0%
Other values (46) 77
38.7%
2024-12-14T18:12:15.596096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
; 743
44.3%
1 186
 
11.1%
2 176
 
10.5%
3 141
 
8.4%
4 113
 
6.7%
5 92
 
5.5%
6 62
 
3.7%
T 41
 
2.4%
7 41
 
2.4%
R 34
 
2.0%
Other values (6) 47
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
; 743
44.3%
1 186
 
11.1%
2 176
 
10.5%
3 141
 
8.4%
4 113
 
6.7%
5 92
 
5.5%
6 62
 
3.7%
T 41
 
2.4%
7 41
 
2.4%
R 34
 
2.0%
Other values (6) 47
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
; 743
44.3%
1 186
 
11.1%
2 176
 
10.5%
3 141
 
8.4%
4 113
 
6.7%
5 92
 
5.5%
6 62
 
3.7%
T 41
 
2.4%
7 41
 
2.4%
R 34
 
2.0%
Other values (6) 47
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
; 743
44.3%
1 186
 
11.1%
2 176
 
10.5%
3 141
 
8.4%
4 113
 
6.7%
5 92
 
5.5%
6 62
 
3.7%
T 41
 
2.4%
7 41
 
2.4%
R 34
 
2.0%
Other values (6) 47
 
2.8%

Posti letto
Real number (ℝ)

High correlation 

Distinct196
Distinct (%)43.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean114.12889
Minimum7
Maximum922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-12-14T18:12:15.671257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile17
Q125
median60
Q3137.25
95-th percentile440.1
Maximum922
Range915
Interquartile range (IQR)112.25

Descriptive statistics

Standard deviation141.01355
Coefficient of variation (CV)1.235564
Kurtosis8.0612704
Mean114.12889
Median Absolute Deviation (MAD)38
Skewness2.6096277
Sum51358
Variance19884.821
MonotonicityNot monotonic
2024-12-14T18:12:15.750055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 35
 
7.8%
20 15
 
3.3%
24 14
 
3.1%
23 11
 
2.4%
32 9
 
2.0%
21 9
 
2.0%
22 8
 
1.8%
16 8
 
1.8%
48 7
 
1.6%
99 7
 
1.6%
Other values (186) 327
72.5%
ValueCountFrequency (%)
7 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
12 4
0.9%
14 3
 
0.7%
15 3
 
0.7%
16 8
1.8%
17 6
1.3%
18 6
1.3%
ValueCountFrequency (%)
922 1
0.2%
864 1
0.2%
792 1
0.2%
736 1
0.2%
725 1
0.2%
650 1
0.2%
646 1
0.2%
636 1
0.2%
623 1
0.2%
577 1
0.2%

Posti letto per piano
Text

Missing 

Distinct219
Distinct (%)63.5%
Missing106
Missing (%)23.5%
Memory size3.7 KiB
2024-12-14T18:12:15.865083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length6.5884058
Min length1

Characters and Unicode

Total characters2273
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)59.1%

Sample

1st row518
2nd row25
3rd row24;19;13
4th row0
5th row5;38;40;40
ValueCountFrequency (%)
0 108
31.3%
25 4
 
1.2%
12 3
 
0.9%
24 3
 
0.9%
23 3
 
0.9%
35 2
 
0.6%
17 2
 
0.6%
22 2
 
0.6%
0;0 2
 
0.6%
18 2
 
0.6%
Other values (209) 214
62.0%
2024-12-14T18:12:16.069721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
; 633
27.8%
1 392
17.2%
2 271
11.9%
0 220
 
9.7%
3 142
 
6.2%
6 139
 
6.1%
4 122
 
5.4%
5 93
 
4.1%
7 89
 
3.9%
8 88
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
; 633
27.8%
1 392
17.2%
2 271
11.9%
0 220
 
9.7%
3 142
 
6.2%
6 139
 
6.1%
4 122
 
5.4%
5 93
 
4.1%
7 89
 
3.9%
8 88
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
; 633
27.8%
1 392
17.2%
2 271
11.9%
0 220
 
9.7%
3 142
 
6.2%
6 139
 
6.1%
4 122
 
5.4%
5 93
 
4.1%
7 89
 
3.9%
8 88
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
; 633
27.8%
1 392
17.2%
2 271
11.9%
0 220
 
9.7%
3 142
 
6.2%
6 139
 
6.1%
4 122
 
5.4%
5 93
 
4.1%
7 89
 
3.9%
8 88
 
3.9%

Tipo attività struture extra
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)0.7%
Missing10
Missing (%)2.2%
Memory size3.7 KiB
Albergo
386 
Residence
51 
albergo
 
4

Length

Max length9
Median length7
Mean length7.2312925
Min length7

Characters and Unicode

Total characters3189
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbergo
2nd rowAlbergo
3rd rowAlbergo
4th rowAlbergo
5th rowAlbergo

Common Values

ValueCountFrequency (%)
Albergo 386
85.6%
Residence 51
 
11.3%
albergo 4
 
0.9%
(Missing) 10
 
2.2%

Length

2024-12-14T18:12:16.148266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T18:12:16.208920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
albergo 390
88.4%
residence 51
 
11.6%

Most occurring characters

ValueCountFrequency (%)
e 543
17.0%
l 390
12.2%
r 390
12.2%
b 390
12.2%
g 390
12.2%
o 390
12.2%
A 386
12.1%
R 51
 
1.6%
s 51
 
1.6%
i 51
 
1.6%
Other values (4) 157
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 543
17.0%
l 390
12.2%
r 390
12.2%
b 390
12.2%
g 390
12.2%
o 390
12.2%
A 386
12.1%
R 51
 
1.6%
s 51
 
1.6%
i 51
 
1.6%
Other values (4) 157
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 543
17.0%
l 390
12.2%
r 390
12.2%
b 390
12.2%
g 390
12.2%
o 390
12.2%
A 386
12.1%
R 51
 
1.6%
s 51
 
1.6%
i 51
 
1.6%
Other values (4) 157
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 543
17.0%
l 390
12.2%
r 390
12.2%
b 390
12.2%
g 390
12.2%
o 390
12.2%
A 386
12.1%
R 51
 
1.6%
s 51
 
1.6%
i 51
 
1.6%
Other values (4) 157
 
4.9%

Interactions

2024-12-14T18:12:11.236116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.420388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.746603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.276004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.607751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.918878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.280271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.472976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.795786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.326261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.652980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.972539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.337416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.524376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.984440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.386289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.714831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.025232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.397276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.582997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.038805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.441086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.767611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.082133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.446771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.632539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.089163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.498090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.818155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.130120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.499867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:09.699022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.147191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.551910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:10.868974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T18:12:11.181961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-14T18:12:16.374353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
CamereCategoriaCivicoCodice viaPiani totaliPosti lettoTipo attività struture extraTipo viaZD
Camere1.0000.305-0.090-0.1610.7650.9820.1730.043-0.066
Categoria0.3051.0000.0000.1410.2770.2890.0790.1180.149
Civico-0.0900.0001.0000.133-0.085-0.0950.0000.0520.089
Codice via-0.1610.1410.1331.000-0.165-0.1570.0710.0960.599
Piani totali0.7650.277-0.085-0.1651.0000.7560.0000.064-0.221
Posti letto0.9820.289-0.095-0.1570.7561.0000.1300.086-0.071
Tipo attività struture extra0.1730.0790.0000.0710.0000.1301.0000.0000.112
Tipo via0.0430.1180.0520.0960.0640.0860.0001.0000.102
ZD-0.0660.1490.0890.599-0.221-0.0710.1120.1021.000

Missing values

2024-12-14T18:12:11.574533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-14T18:12:11.703164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-14T18:12:11.834235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

UbicazioneTipo viaDescrizione viaCivicoCodice viaZDCamereCamere pianoCategoriaInsegnaPiani totaliPiano pianoPosti lettoPosti letto per pianoTipo attività struture extra
0ALZ NAVIGLIO GRANDE N. 8 (z.d. 6)ALZNAVIGLIO GRANDE8.05144.06.014.0NaN4HOTEL MAISON BORELLANaNNaN25.0NaNAlbergo
1codvia 0000 num.024 ; ()NaNNaNNaNNaNNaN259.02594radisson blu hotel milanNaNNaN518.0518Albergo
2CSO BUENOS AIRES N. 18 (z.d. 3)CSOBUENOS AIRES18.02129.03.016.0161hotel aurora1.0125.025Albergo
3CSO BUENOS AIRES N. 26 (z.d. 3)CSOBUENOS AIRES26.02129.03.025.0NaN3hotel buenos airesNaNNaN39.0NaNAlbergo
4CSO BUENOS AIRES N. 2 (z.d. 3)CSOBUENOS AIRES2.02129.03.046.015;11;83albergo fenice4.01;2;3;498.024;19;13Albergo
5CSO BUENOS AIRES N. 33 (z.d. 3)CSOBUENOS AIRES33.02129.03.065.004GALAXY G SRLNaNNaN97.00Albergo
6CSO BUENOS AIRES N. 3 (z.d. 3)CSOBUENOS AIRES3.02129.03.0116.04;23;24;244cristoforo colombo4.02;3;4;5191.05;38;40;40Albergo
7CSO COLOMBO CRISTOFORO N. 15 (z.d. 6)CSOCOLOMBO CRISTOFORO15.05114.06.039.0NaN3hotel minervaNaNNaN60.0NaNAlbergo
8CSO COLOMBO CRISTOFORO N. 15 (z.d. 6)CSOCOLOMBO CRISTOFORO15.05114.06.044.003hotel minervaNaNNaN67.00Albergo
9CSO CONCORDIA N. 1 (z.d. 3)CSOCONCORDIA1.03116.03.077.0NaN5NaNNaNNaN180.0NaNAlbergo
UbicazioneTipo viaDescrizione viaCivicoCodice viaZDCamereCamere pianoCategoriaInsegnaPiani totaliPiano pianoPosti lettoPosti letto per pianoTipo attività struture extra
441VLE STURZO DON LUIGI N. 45 (z.d. 9)VLESTURZO DON LUIGI45.01704.09.0420.0704atahotel executive6.01;2;3;4;5;6792.0140Albergo
442VLE SUZZANI GIOVANNI N. 13 (z.d. 9)VLESUZZANI GIOVANNI13.01446.09.0172.030;114novotel milano nord7.01;2;3;4;5;6;7344.060;22Albergo
443VLE SUZZANI GIOVANNI num.013/15 ; (z.d. 9)NaNNaNNaNNaNNaN131.0333hotel ibis milano ca granda4.01;2;3;4262.066Albergo
444VLE TESTI FULVIO N. 300 (z.d. 9)VLETESTI FULVIO300.01441.09.0140.015;15;15;15;15;94starhotels touristNaNR;1;2;3;4;5250.029;29;29;29;29;9Albergo
445VLE TUNISIA N. 6 (z.d. 3)VLETUNISIA6.02121.03.012.001hotel san tomaso1.0NaN25.00Albergo
446VLE TUNISIA N. 6 (z.d. 3)VLETUNISIA6.02121.03.013.0161hotel kennedy1.0623.023Albergo
447VLE TUNISIA N. 9 (z.d. 3)VLETUNISIA9.02121.03.050.03;6;9;9;94st. george hotelNaNA;1;2;3;499.05;16;26;26;26Albergo
448VLE VITTORIO VENETO N. 30 (z.d. 2)VLEVITTORIO VENETO30.02107.02.015.0NaN2hotel casa miaNaNNaN25.0NaNAlbergo
449VLE ZARA N. 1 (z.d. 9)VLEZARA1.01170.09.032.0NaN4casa albergo residence zara lagostaNaNNaN64.0NaNResidence
450VLE ZARA N. 89 (z.d. 9)VLEZARA89.01170.09.022.012;7;43hotel gala3.0R;1;235.020;12;7Albergo